Global capitalism is significantly impacted by private markets. Every year, they transfer trillions to funds and investments, frequently directing them toward high-tech development projects. However, the funds themselves only invest between a third and a half of what publicly visible financial firms commit to innovation as a percentage of their income in technology. Since the majority of funds’ inception, the legacy methodologies’ residual effects have impeded data management and the investor experience. This bottleneck, which exists right where capital enters the system, has remained throughout the life of the funds, confusing both investors and fund managers.

The sign of pain and its underlying causes (data fragmentation)

Private markets, a driving force behind investments in technological innovation, have long needed to digitally alter key aspects of fund administration and capital raising. These processes are also essential for deal execution and compliance. Almost every participant has experienced the slowness of outdated paperwork while onboarding investors, including investors (limited partners, or LPs), fund managers, general partners, and their lawyers and fund administrators. Due to a talent shortage and the need to scale for a larger LP market that includes retail investors, relying on PDF forms, Excel spreadsheets, and manual processes has become more challenging recently.

More funds have expedited their implementation of workflow automation since COVID-19, which is a significant improvement but not the complete answer. This is due to the fact that the industry’s long-standing sediment layers of disorganized data present a significant barrier to maximizing fund development and interactions with limited partners (LPs). Different portfolio companies, regulatory agencies, funds or fund families, and investors all arrange and view their data in different ways.

The solution to that problem requires strategic architecture decisions and data “translation.”

Transforming private markets, beginning with fund creation

Process automation may significantly enhance investors’ experiences, lower data input errors, satisfy compliance needs, and manage the LP life cycle. Workflow for gathering the necessary data takes the role of onerous, friction-filled steps for qualifying and onboarding investors. Additionally, it performs data integrity checks and assists investors in appropriately entering their information. Funds may shorten the onboarding process, reduce friction, hasten fund formation, and provide investors the lavish treatment they deserve. This is tempting for fund managers since private equity investments have stalled.

An automated platform can capture and validate data once, transfer it automatically, and prevent transcribing errors, as it does in many sectors. This lowers processing expenses while simultaneously enhancing the downstream data quality and throughput.

Address data discrepancy directly or indirectly?

When fund operations are going smoothly, it becomes clear that every fund has its own data model and that portfolio firms have their own reporting structures. The ideal answer for private markets would be an industry-wide standardized data protocol, but this goal is elusive and will need support from many different parties. Therefore, it is the responsibility of practitioners and software providers to adopt tools and techniques to normalize data and circumvent the disjointed, inconsistent data structures. Making careful architectural trade-offs between being prescriptive (“our way, or no way”) and more adaptive (“your way, when necessary”) are required while building this type of platform.

A workflow solution must strike a balance between having a standard, predetermined method and the flexibility to adjust it to fit the procedures of certain funds. Particularly with larger funds, greater customization is frequently necessary. Remember that a solution will need to adapt to meet shifting compliance needs; it is crucial to confirm that each investor is qualified and complies with SEC regulations and to keep the fund in line with its fiduciary duties to investors.

Newer technology will support solutions from the private market

With expectations rising, no fund manager wants to fall behind. Workflow platforms offer a common place to start, especially if they incorporate domain-specific business logic. As private markets embrace digital transformation, cutting-edge technology is likely to be included into them.

In the future, blockchain might wind up acting as a “industry ledger” for transactions between private markets. Additionally, it is probably going to be beneficial for KYC and AML, decreasing the need for data duplication, facilitating the tracking of financial activities, and advancing the cause of standardized, explicit due diligence standards. Blockchain for securities transactions has already seen considerable testing. Funds must embrace a common data protocol for blockchain to play a significant role in private markets. A procedure like that is the industry’s illusive holy grail. Additionally, blockchain technologies need to develop further and get past well-known flaws in performance, scalability, etc.

Beyond qualification and onboarding, RPA (robotic process automation) can update how funds interact with their LPs. RPA technologies, which function more or less like bot programmes, can automate rote processes that are carried out on antiquated legacy systems. These crucial financial procedures can be automated by RPA since they cannot be easily retired or replaced. RPA may significantly reduce the amount of time spent on repetitive tasks in lean back-office operations, freeing up staff to undertake work of a higher order. In the end, private market-focused RPA bots can assist in offloading GP/LP relationship components including batch routing transactional papers and compiling monthly reports.

RPAs’ potential may be further unlocked by AI and ML if they are combined with greater analysis and comprehension of the situation. AI may make decisions and give the workhorse bots instructions, enhancing their impact and introducing use cases to tackle more complicated problems. As long as the data is gathered, AI ought to be adept at quickly analyzing and sorting through massive amounts of data. How to ensure that data is ready is a typical AI problem that necessitates substantial data collecting and stringent human training. When AI systems are implemented inside of businesses, these difficult requirements are frequently disregarded. AI-driven solutions are anticipated to increase compliance, diligence, and KYC/AML from the back office and offer potent dynamics for seeking out transaction prospects from the front office with sufficient access to data from across the industry.

Without the need for software professionals, low-code and no-code (LCNC) solutions enable platform updates and customization to match fund-specific processes. The current generation of legacy solutions are stiff, monolithic, and frequently hard-coded, making it challenging or impossible to adapt them to conform to modern standards. As additional funds, portfolio firms, and features are added to digital transformation programmes, these technologies aid in addressing the difficulty of data standardization.

LCNC promises quick configuration and deployment of pre-engineered software modules for specific internal workflow use cases. Business or IT expertise can create simple standalone applications for processing investor data and documentation on the backend with little to no programming resources. This comes with the warning that no-code systems would be dangerous if connecting directly with external clients, less portable or scalable, and harder to handle edge cases. Given sufficient funding, a mix of low-code and no-code solutions might be able to close some reporting and compliance gaps that exist between legacy processes and the requirements of the present for managing a fund.

Private market funds are fundamentally altering how they run by implementing the first phase in digital transformation, workflow automation, which eliminates friction and wasted time from the investment process. Investor satisfaction has increased at the same time that data quality and compliance confidence have both increased. The advancements made by private market funds during the first stage of innovation can be continued in the future with adaptive design and multilayer data translation employing new technologies.